library(tidyverse)
library(readxl)
library(skimr)
library(plotly)
library(gghighlight)
library(patchwork)
date_caption <- "9 janvier 2024"
source("tools/themes.R") # themes
source <- read_csv("posts/2025-01-21/data/antibes-animations-sportives.csv")
df <- sourceIntroduction
Résultats
Code
Import
Inspect
head(df)
unique(df$ORGANISATEUR)
colnames(df)
glimpse(df)
max(df$`DATE FIN`)Clean
# standardize cols names and variables in chr type
df <-
df %>%
rename_with(tolower) %>%
rename_with(~ str_squish(.)) %>%
rename_with(~ str_replace_all(., " ", "_")) %>%
mutate(across(where(is.character), ~ str_squish(str_to_lower(.)))) %>%
mutate(across(c(date_debut, date_fin), as.Date))
# drop NA
df <-
df %>%
drop_na(date_debut, date_fin, organisateur)
# check no NAs are remaining
skim(df)
# remove unwanted cols
df <-
df %>%
select(animations, date_debut, date_fin, organisateur, lieu_1)
# adding useful cols
df <-
df %>% mutate(yd = year(date_debut),
md = month(date_debut),
dd = day(date_debut),
yf = year(date_fin),
mf = month(date_fin),
df = day(date_fin),
yearmonth = paste0(yd,"-", md)
) %>% arrange(yd, md, dd) %>%
mutate(duration = (date_fin - date_debut)
)Further cleaning using OpenRefine
# save file for OpenRefine
write_csv(df, "posts/2025-01-21/data/animations.csv")
# load refined data
df_refined <- read_csv("posts/2025-01-21/data/animations_openrefined.csv")
# additional cleaning step
df_refined <-
df_refined %>%
mutate(organisateur = case_when(
organisateur %in% c("asf boules", "as fontonne boule") ~ "as fontonne boules",
TRUE ~ organisateur
))
# filter year
df_refined <-
df_refined %>% filter(yd > 2019)
# title case variables
df_refined <-
df_refined %>%
mutate(across(c(lieu_1, organisateur), str_to_title))df_refined# A tibble: 603 × 13
animations date_debut date_fin organisateur lieu_1 yd md dd yf
<chr> <date> <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 bain du jo… 2020-01-01 2020-01-01 Day One Plage… 2020 1 1 2020
2 rencontre … 2020-01-04 2020-01-04 Badminton C… Gymna… 2020 1 4 2020
3 match pare… 2020-01-12 2020-01-12 Oajlp Handb… Salle… 2020 1 12 2020
4 ag wild mo… 2020-01-12 2020-01-12 Wild Move Base … 2020 1 12 2020
5 ski voile … 2020-01-16 2020-01-19 Société Des… Port … 2020 1 16 2020
6 19ème souv… 2020-01-19 2020-01-19 Antibes Vél… Parki… 2020 1 19 2020
7 rencontre … 2020-01-21 2020-01-24 Asf Hockey … Stade… 2020 1 21 2020
8 challenge … 2020-01-22 2020-01-22 Handisport … Salle… 2020 1 22 2020
9 sélection … 2020-02-01 2020-02-01 Oajlp Gymna… Salle… 2020 2 1 2020
10 top 12 gym 2020-02-08 2020-02-08 Oajlp Gymna… Foch 2020 2 8 2020
# ℹ 593 more rows
# ℹ 4 more variables: mf <dbl>, df <dbl>, yearmonth <chr>, duration <dbl>Analysis
# faceting : top organizers, locations, activities
orgs <-
df_refined %>%
count(organisateur) %>% arrange(-n)
orgs
lieux <-
df_refined %>%
count(lieu_1) %>% arrange(-n)
lieux
activities <-
df_refined %>%
count(animations) %>% arrange(-n)
activities
# Filter top 10 organisateurs
top_orgs <- orgs %>%
slice_max(order_by = n, n = 10)
# Filter top 10 lieux
top_lieux <- lieux %>%
slice_max(order_by = n, n = 10)
# Count
count.animations.year <-
df_refined %>%
count(yd)
count.animations.yearmonth <-
df_refined %>%
count(yearmonth)
# Averages
mean(df_refined$duration) # mean duration
skim(df_refined$duration) # other statsPlot
# Color
c_highlight <-"#1D6FA1"
## Col chart per month
df_plot.year.facet <-
df_refined %>%
group_by(yd, md, organisateur) %>%
count() %>%
mutate(yd = factor(yd, levels = c("2023", "2022", "2021", "2020")))
plot.year.facet <-
df_plot.year.facet %>%
ggplot(aes(x = md, y = n)) +
geom_col(fill = c_highlight, alpha = 0.6) +
geom_col(data = df_plot.year.facet %>% filter(yd == 2023), fill = c_highlight, alpha = 1) +
geom_text( # axis labels
data = data.frame(x = 12.9, y = seq(0, 40, by = 10)),
aes(x, y, label = y),
color = "black",
family = setfont,
hjust = 1,
vjust = -0.5,
size = 3.5
) +
geom_text(data = count.animations.year %>% filter(yd == 2023),
aes(x = 2, y = 34, label = paste0(n, " activités en 2023")),
family = setfont,
hjust = 0.3,
fontface = "bold",
color = c_highlight,
size = 6) +
geom_text(data = count.animations.year %>% filter(yd < 2023),
aes(x = 2, y = 34, label = paste0(n, " activités")),
family = setfont,
color = c_highlight,
hjust = 0.5,
alpha = 0.6,
size = 6) +
scale_x_continuous( limits = c(0, 13),
expand = c(0, 0),
breaks = seq(1, 12, by = 1),
labels = c("J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"),
) +
scale_y_continuous(expand = c(0, 0)) + #start axis origin at 0
facet_wrap(~yd, ncol = 1, strip.position = "left", scales = 'free_x') +
theme(legend.position = "none")
p <-
plot.year.facet +
labs(title = "Moins d'activités sportives à Antibes en 2023 par rapport à 2022",
subtitle = "Nombre d'activités sportives organisées à Antibes par mois et par années.",
caption = social_caption2) + tt2
# Bar charts for top 10s
# Plot for top 10 organisateurs
plot_orgs <- ggplot(top_orgs, aes(x = reorder(organisateur, n), y = n)) +
geom_bar(stat = "identity", fill = "#457ECD") +
geom_text(aes(label = n), hjust = 1.8, size = 8, family = setfont, fontface = "bold", color = "white") +
coord_flip() +
labs(
title = "Top 10 des organisateurs d'activités sportives à Antibes",
subtitle = "Classement des organisations sportives d'Antibes Juan-Les-Pins, par nombre d'activités organisées. Période 2020-2023",
caption = social_caption2
) + tt4
# Plot for top 10 lieux
plot_lieux <- ggplot(top_lieux, aes(x = reorder(lieu_1, n), y = n)) +
geom_bar(stat = "identity", fill = "#FC972F") +
geom_text(aes(label = n), hjust = 1.8, size = 8, family = setfont, fontface = "bold", color = darkblue) +
coord_flip() +
labs(
title = "Top 10 des lieux d'activités sportives à Antibes",
subtitle = "Classement des lieux d'Antibes Juan-Les-Pins, par nombre d'activités sportives acceuillies. <br>Période 2020-2023",
caption = social_caption2
) + tt4Render
plot_orgs 
plot_lieux 
p
Source
Manifestations sportives à Antibes Juan-les-Pins, disponible sur data.gouv.fr. Mise à jour du 9 janvier 2024.